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library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)

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library(readxl)
hardship_complete <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/data/Hardship_complete.xlsx")
hardship_complete
NA
NA
hardship_complete$mean_homicide=log(hardship_complete$mean_homicide)
hardship_complete$gdp=log(hardship_complete$gdp)
hardship_complete$Infant_mortality=log(hardship_complete$Infant_mortality)
hardship_complete$life_expect=log(hardship_complete$life_expect)
hardship_complete$gini_income=log(hardship_complete$gini_income)
hardship_complete$`primary_ female_ enrollment_ rate`=log(hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

# Reverse Codierung
hardship_complete$mean_homicide=scale(hardship_complete$mean_homicide)
hardship_complete$gdp=scale(-hardship_complete$gdp)
hardship_complete$Infant_mortality=scale(hardship_complete$Infant_mortality)
hardship_complete$life_expect=scale(-hardship_complete$life_expect)
hardship_complete$gini_income=scale(hardship_complete$gini_income)
hardship_complete$gini_income=scale(-hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

hardship_complete$hardship_index=(hardship_complete$mean_homicide+hardship_complete$gdp+hardship_complete$gini_income+hardship_complete$life_expect+hardship_complete$Infant_mortality+hardship_complete$`primary_ female_ enrollment_ rate`)/6

hardship_complete
NA
NA

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df
#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)
  [1] "wave"          "V1A"           "V1B"           "country_code"  "V2A"           "V3"            "V4"            "V4_CO"         "V5"           
 [10] "V5_CO"         "V6"            "V6_CO"         "V7"            "V7_CO"         "V8"            "V8_CO"         "V9"            "V9_CO"        
 [19] "V10"           "V11"           "V12"           "V13"           "V14"           "V15"           "V16"           "V17"           "V18"          
 [28] "V19"           "V20"           "V21"           "V22"           "V23"           "V24"           "V25"           "V26"           "V27"          
 [37] "V28"           "V29"           "V30"           "V31"           "V32"           "V33"           "V34"           "V35"           "V36"          
 [46] "V37"           "V38"           "V39"           "V40"           "V41"           "V42"           "V43"           "V43_01"        "V43_02"       
 [55] "V43_03"        "V43_04"        "V43_05"        "V43_06"        "V43_07"        "V43_08"        "V43_09"        "V43_10"        "V43_11"       
 [64] "V43_12"        "V43_13"        "V43_14"        "V43_15"        "V43_16"        "V43_17"        "V43_18"        "V43_19"        "V43_20"       
 [73] "V43_21"        "V43_22"        "V43_23"        "V43_24"        "V43_25"        "V43_26"        "V43_27"        "V43_28"        "V43_29"       
 [82] "V43_30"        "V44"           "V45"           "V46"           "V47"           "V48"           "V49"           "V50"           "V51"          
 [91] "V52"           "V53"           "V54"           "married"       "children"      "V57"           "V58"           "V59"           "V60"          
[100] "V61"           "V62"           "V63"           "V64"           "V65"           "V66"           "V67"           "V68"           "V69"          
[109] "V69_HK"        "V70"           "V70_HK"        "V71"           "V72"           "V73"           "V73_HK"        "V74"           "V74_HK"       
[118] "V75"           "V76"           "V77"           "V78"           "V79"           "V80"           "V81"           "V82"           "V83"          
[127] "V84"           "V85"           "risktaking"    "V87"           "V88"           "V89"           "V90"           "V91"           "V92"          
[136] "V93"           "V94"           "V95"           "V96"           "V97"           "V98"           "V99"           "V100"          "V101"         
[145] "V102"          "V103"          "V104"          "V105"          "V106"          "V107"          "V108"          "V109"          "V110"         
[154] "V111"          "V112"          "V113"          "V114"          "V115"          "V116"          "V117"          "V118"          "V119"         
[163] "V120"          "V121"          "V122"          "V123"          "V124"          "V125"          "V126"          "V127"          "V128"         
[172] "V129"          "V130"          "V130_CA_1"     "V130_IQ_1"     "V130_IQ_2"     "V130_IQ_3"     "V130_IQ_4"     "V130_NZ_1"     "V130_NZ_2"    
[181] "V131"          "V132"          "V133"          "V134"          "V135"          "V136"          "V137"          "V138"          "V139"         
[190] "V140"          "V141"          "V142"          "V143"          "V144"          "V145"          "V146_00"       "V146_01"       "V146_02"      
[199] "V146_03"       "V146_04"       "V146_05"       "V146_06"       "V146_07"       "V146_08"       "V146_09"       "V146_10"       "V146_11"      
[208] "V146_12"       "V146_13"       "V146_14"       "V146_15"       "V146_16"       "V146_17"       "V146_18"       "V146_19"       "V146_20"      
[217] "V146_21"       "V146_22"       "V147"          "V148"          "V149"          "V150"          "V151"          "V151_IQ_A"     "V151_IQ_B"    
[226] "V152"          "V153"          "V154"          "V155"          "V156"          "V157"          "V158"          "V159"          "V160"         
[235] "V161"          "V162"          "V163"          "V164"          "V165"          "V166"          "V167"          "V168"          "V169"         
[244] "V170"          "V171"          "V172"          "V173"          "V174"          "V175"          "V176"          "V177"          "V178"         
[253] "V179"          "V180"          "V181"          "V182"          "V183"          "V184"          "V185"          "V186"          "V187"         
[262] "V188"          "V189"          "V190"          "V191"          "V192"          "V193"          "V194"          "V195"          "V196"         
[271] "V197"          "V198"          "V199"          "V200"          "V201"          "V202"          "V203"          "V204"          "V205"         
[280] "V206"          "V207"          "V208"          "V209"          "V210"          "V211"          "V212"          "V213A"         "V213B"        
[289] "V213C"         "V213D"         "V213E"         "V213F"         "V213G"         "V213H"         "V213K"         "V213L"         "V213M"        
[298] "V213N"         "V214"          "V215"          "V216"          "V217"          "V218"          "V219"          "V220"          "V221"         
[307] "V222"          "V223"          "V224"          "V225"          "V226"          "V227"          "V228"          "V229"          "V230"         
[316] "V231"          "V232"          "V233"          "V233A"         "V234"          "gender"        "V236"          "age"           "education"    
[325] "V238CS"        "V239"          "V240"          "employed"      "V242"          "V242A_CO"      "V243"          "V244"          "V245"         
[334] "V246"          "V247"          "V248"          "V249"          "V250"          "V251"          "V252"          "V252B"         "V253"         
[343] "V253CS"        "V254"          "V255"          "V255CS"        "V256"          "V257"          "V257B"         "V257C"         "V258"         
[352] "V259"          "V259A"         "V260"          "V261"          "V262"          "V263"          "V264"          "V265"          "S024"         
[361] "S025"          "Y001"          "Y002"          "Y003"          "SACSECVAL"     "SECVALWGT"     "RESEMAVAL"     "WEIGHTB"       "I_AUTHORITY"  
[370] "I_NATIONALISM" "I_DEVOUT"      "DEFIANCE"      "WEIGHT1A"      "I_RELIGIMP"    "I_RELIGBEL"    "I_RELIGPRAC"   "DISBELIEF"     "WEIGHT2A"     
[379] "I_NORM1"       "I_NORM2"       "I_NORM3"       "RELATIVISM"    "WEIGHT3A"      "I_TRUSTARMY"   "I_TRUSTPOLICE" "I_TRUSTCOURTS" "SCEPTICISM"   
[388] "WEIGHT4A"      "I_INDEP"       "I_IMAGIN"      "I_NONOBED"     "AUTONOMY"      "WEIGHT1B"      "I_WOMJOB"      "I_WOMPOL"      "I_WOMEDU"     
[397] "EQUALITY"      "WEIGHT2B"      "I_HOMOLIB"     "I_ABORTLIB"    "I_DIVORLIB"    "CHOICE"        "WEIGHT3B"      "I_VOICE1"      "I_VOICE2"     
[406] "I_VOI2_00"     "VOICE"         "WEIGHT4B"      "S001"          "S007"          "S018"          "S019"          "S021"          "COW"          
#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, married, employed, education)
WV5_data
# Read countrynames data from the CSV file (to decode the dataset 5)
countrynames <- read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

            Andorra           Argentina           Australia              Brazil            Bulgaria        Burkina Faso              Canada               Chile 
               1003                1002                1421                1500                1001                1534                2164                1000 
              China            Colombia          Cyprus (G)               Egypt            Ethiopia             Finland              France             Georgia 
               1991                3025                1050                3051                1500                1014                1001                1500 
            Germany               Ghana       Great Britain           Guatemala           Hong Kong             Hungary               India           Indonesia 
               2064                1534                1041                1000                1252                1007                2001                2015 
               Iran                Iraq               Italy               Japan              Jordan            Malaysia                Mali              Mexico 
               2667                2701                1012                1096                1200                1201                1534                1560 
            Moldova             Morocco         Netherlands         New Zealand              Norway                Peru              Poland             Romania 
               1046                1200                1050                 954                1025                1500                1000                1776 
             Russia              Rwanda            Slovenia        South Africa         South Korea               Spain              Sweden         Switzerland 
               2033                1507                1037                2988                1200                1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago              Turkey             Ukraine       United States             Uruguay            Viet Nam 
               1227                1534                1002                1346                1000                1249                1000                1495 
             Zambia 
               1500 
# Display the updated WV5_data
print(WV5_data)
#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)
WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)


#select only the variables of interest

WV6_data <- WV6_data %>%
  dplyr::select(wave, gender, age, country_code,risktaking, children, married, employed, education)
WV6_data
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)

            Algeria           Argentina             Armenia           Australia          Azerbaijan             Belarus              Brazil               Chile 
               1200                1030                1100                1477                1002                1535                1486                1000 
              China            Colombia          Cyprus (G)             Ecuador               Egypt             Estonia             Georgia             Germany 
               2300                1512                1000                1202                1523                1533                1202                2046 
              Ghana               Haiti           Hong Kong               India                Iraq               Japan              Jordan          Kazakhstan 
               1552                1996                1000                4078                1200                2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon               Libya            Malaysia              Mexico             Morocco         Netherlands 
               1303                1500                1200                2131                1300                2000                1200                1902 
        New Zealand             Nigeria            Pakistan           Palestine                Peru         Philippines              Poland               Qatar 
                841                1759                1200                1000                1210                1200                 966                1060 
            Romania              Russia              Rwanda           Singapore            Slovenia        South Africa         South Korea               Spain 
               1503                2500                1527                1972                1069                3531                1200                1189 
             Sweden              Taiwan            Thailand Trinidad and Tobago             Tunisia              Turkey             Ukraine       United States 
               1206                1238                1200                 999                1205                1605                1500                2232 
            Uruguay          Uzbekistan               Yemen            Zimbabwe 
               1000                1500                1000                1500 
WV6_data
range(WVS_data$age)
[1] 15 99
WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave5 = subset(WV5_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave6 = subset(WV6_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
WVS_data <- na.omit(WVS_data)
data_Wave5 <- na.omit(data_Wave5)
data_Wave6 <- na.omit(data_Wave6)


# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))
# Transfrom risk item such that high values represent more risk taking
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data

WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 


hardship <- hardship_complete %>%
  dplyr::select(country, isocode, hardship_index)
hardship
WVS_mixed_model <- left_join(WVS_data, hardship, by = "country")
WVS_mixed_model
head(WVS_mixed_model)
library(lmerTest)

Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step
# intercept only model
model0 = lmer(Z_score_risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)
summary_model0
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Z_score_risktaking ~ 1 + (1 | country)
   Data: WVS_mixed_model

REML criterion at convergence: 406605.8

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3179 -0.8254 -0.0722  0.7614  2.5318 

Random effects:
 Groups   Name        Variance Std.Dev.
 country  (Intercept) 0.08622  0.2936  
 Residual             0.90218  0.9498  
Number of obs: 148527, groups:  country, 76

Fixed effects:
            Estimate Std. Error       df t value Pr(>|t|)
(Intercept)  0.01243    0.03379 75.10025   0.368    0.714
# age, sex 
model1 = lmer(T_score_risktaking ~ 1 +scale(z_score_age)+factor(gender) + (1+scale(z_score_age)+factor(gender)|country),data = WVS_mixed_model)
summary_model1=summary(model1)
summary_model1
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) +      (1 + scale(z_score_age) + factor(gender) | country)
   Data: WVS_mixed_model

REML criterion at convergence: 1081381

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.4657 -0.7816 -0.0765  0.7536  3.2215 

Random effects:
 Groups   Name               Variance Std.Dev. Corr     
 country  (Intercept)         7.6729  2.7700            
          scale(z_score_age)  0.8133  0.9018   0.39     
          factor(gender)1     0.8723  0.9340   0.19 0.33
 Residual                    84.6041  9.1981            
Number of obs: 148527, groups:  country, 76

Fixed effects:
                   Estimate Std. Error      df t value Pr(>|t|)    
(Intercept)         51.3360     0.3200 75.2652  160.42   <2e-16 ***
scale(z_score_age)  -1.9148     0.1068 74.8609  -17.93   <2e-16 ***
factor(gender)1     -2.3166     0.1193 72.3578  -19.43   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) sc(__)
scl(z_scr_) 0.373        
fctr(gndr)1 0.135  0.287 
#model 2
library(lme4)

library(lme4)

# Define the lmer model and assign it to 'model_2'
model_2 <- lmer(T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) + factor(children) + 
                 factor(married) + factor(employed) + factor(education) + 
                 (1 + scale(z_score_age) + factor(gender) + factor(children) + 
                  factor(married) + factor(employed) + factor(education) | country),
                data = WVS_data)

# Display the summary of the model
summary(model_2)
anova(model0,model1)
refitting model(s) with ML (instead of REML)
Data: WVS_mixed_model
Models:
model0: Z_score_risktaking ~ 1 + (1 | country)
model1: T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) + (1 + scale(z_score_age) + factor(gender) | country)
       npar     AIC     BIC  logLik deviance Chisq Df Pr(>Chisq)
model0    3  406607  406637 -203300   406601                    
model1   10 1081396 1081495 -540688  1081376     0  7          1
anova(model1,model_2)
refitting model(s) with ML (instead of REML)
coefsallmodels=rbind(summary_model1$coefficients,
summary_model_2$coefficients,
summary_model_3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")
library(dplyr)

# Assuming your dataset is named 'data' and contains columns: location, age, cause, val

# Step 1: Filter data for a specific mental disorder (e.g., Anxiety Disorders)
filtered_data <- gbd_mentalhealth %>%
  filter(cause == "Anxiety disorders")  # Change "Anxiety Disorders" to the desired disorder

# Step 2: Calculate the mean of 'val' (Disability-Adjusted Life Years) across locations and ages
mean_DALYs <- mean(filtered_data$val)

# Optionally, if you want to calculate mean by country:
mean_by_country <- filtered_data %>%
  group_by(country) %>%
  summarise(mean_Anxiety_disorders = mean(val))

# View the mean DALYs for the filtered disorder
print(mean_DALYs)
[1] 2637.681
# View the mean DALYs by country for the filtered disorder
print(mean_by_country)
NA

```

---
title: "R Notebook"
output: html_notebook
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)
```

Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Cmd+Option+I*.

When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Cmd+Shift+K* to preview the HTML file). 

The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.

```{r}
library(readxl)
hardship_complete <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/data/Hardship_complete.xlsx")
hardship_complete


```
```{r}
hardship_complete$mean_homicide=log(hardship_complete$mean_homicide)
hardship_complete$gdp=log(hardship_complete$gdp)
hardship_complete$Infant_mortality=log(hardship_complete$Infant_mortality)
hardship_complete$life_expect=log(hardship_complete$life_expect)
hardship_complete$gini_income=log(hardship_complete$gini_income)
hardship_complete$`primary_ female_ enrollment_ rate`=log(hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

# Reverse Codierung
hardship_complete$mean_homicide=scale(hardship_complete$mean_homicide)
hardship_complete$gdp=scale(-hardship_complete$gdp)
hardship_complete$Infant_mortality=scale(hardship_complete$Infant_mortality)
hardship_complete$life_expect=scale(-hardship_complete$life_expect)
hardship_complete$gini_income=scale(hardship_complete$gini_income)
hardship_complete$gini_income=scale(-hardship_complete$`primary_ female_ enrollment_ rate`)
hardship_complete

hardship_complete$hardship_index=(hardship_complete$mean_homicide+hardship_complete$gdp+hardship_complete$gini_income+hardship_complete$life_expect+hardship_complete$Infant_mortality+hardship_complete$`primary_ female_ enrollment_ rate`)/6

hardship_complete


```
```{r}

# Data of Wave 5


WV5_data <- readRDS("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/F00007944-WV5_Data_R_v20180912.rds")


# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df
```

```{r}
#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)



#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, married, employed, education)
WV5_data
```
```{r}
# Read countrynames data from the CSV file (to decode the dataset 5)
countrynames <- read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

# Display the updated WV5_data
print(WV5_data)
```
```{r}
#Read Dataset (Wave 6)

WV6_data <- load("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 
print(WV6_data)
```

```{r}
WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)


#select only the variables of interest

WV6_data <- WV6_data %>%
  dplyr::select(wave, gender, age, country_code,risktaking, children, married, employed, education)
WV6_data
```
```{r}
countrynames = read.csv("/Users/cristinacandido/Documents/Github/risk_wvs/data/WVS/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)
WV6_data
```
```{r}
WV5_data
WV6_data
WVS_data = rbind(WV5_data, WV6_data)
WVS_data

unique(WVS_data$age)
range(WVS_data$age)
```
```{r}
WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave5 = subset(WV5_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
data_Wave6 = subset(WV6_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
WVS_data <- na.omit(WVS_data)
data_Wave5 <- na.omit(data_Wave5)
data_Wave6 <- na.omit(data_Wave6)


# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))
```
```{r}
# Transfrom risk item such that high values represent more risk taking
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

  
# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 

# Assuming T-scores have a mean of 50 and a standard deviation of 10
WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data
```

```{r}

WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 


hardship <- hardship_complete %>%
  dplyr::select(country, isocode, hardship_index)
hardship
```
```{r}
WVS_mixed_model <- left_join(WVS_data, hardship, by = "country")
WVS_mixed_model
head(WVS_mixed_model)
```
```{r}
library(lmerTest)

# intercept only model
model0 = lmer(Z_score_risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)
summary_model0
```
```{r}
# age, sex 
model1 = lmer(T_score_risktaking ~ 1 +scale(z_score_age)+factor(gender) + (1+scale(z_score_age)+factor(gender)|country),data = WVS_mixed_model)
summary_model1=summary(model1)
summary_model1
```

```{r}
#model 2
library(lme4)

library(lme4)

# Define the lmer model and assign it to 'model_2'
model_2 <- lmer(T_score_risktaking ~ 1 + scale(z_score_age) + factor(gender) + factor(children) + 
                 factor(married) + factor(employed) + factor(education) + 
                 (1 + scale(z_score_age) + factor(gender) + factor(children) + 
                  factor(married) + factor(employed) + factor(education) | country),
                data = WVS_data)

# Display the summary of the model
summary(model_2)
```


```{r}
model_3 <- lmer(T_score_risktaking ~ 1 + scale(z_score_age) * hardship_index + 
                    factor(gender) * hardship_index + factor(married) + factor(children) + 
                    factor(education) + factor(employed) + 
                    (1 + scale(z_score_age) + factor(married) + factor(children) + 
                     factor(education) + factor(employed) | country),
                data = WVS_mixed_model)

summary(model_3)




```

```{r}
anova(model0,model1)
anova(model1,model_2)
anova(model_2,model_3) 
```

```{r}
coefsallmodels=rbind(summary_model1$coefficients,
summary_model_2$coefficients,
summary_model_3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")
```


```{r}
# Read the CSV file into a data frame
gbd_mentalhealth <- read_excel("/Users/cristinacandido/Documents/Github/risk_wvs/GBD_mentalhealth.xlsx")

gbd_mentalhealth

gbd_mentalhealth <- gbd_mentalhealth %>%
  rename(gender = sex, country = location)
gbd_mentalhealth


#select only the variables of interest
gbd_mentalhealth <- gbd_mentalhealth %>%
  dplyr::select(country, gender, age, cause, val)
gbd_mentalhealth


library(dplyr)

# Group data by country and age group, and calculate summary statistics
summary_by_country_age <- gbd_mentalhealth %>%
  group_by(country, age) %>%
  summarise(
    mean_DALYs = mean(val))  # Calculate mean of DALYs

library(dplyr)

# Assuming 'summary_by_country_age' contains your summarized dataset
mean_by_country <- summary_by_country_age %>%
  group_by(country) %>%
  summarise(mean_DALYs = mean(mean_DALYs))

# View the resulting mean by country
print(mean_by_country)

#log transform
mean_by_country$mean_DALYs=log(mean_by_country$mean_DALYs)
 
mean_by_country 

#Reverse codierung 
mean_by_country$mean_DALYs=scale(mean_by_country$mean_DALYs)

mean_by_country

#rename mean_DALYS
mental_health_index <- mean_by_country %>%
  rename('mental health' = mean_DALYs)
mental_health_index

```
```{r}
library(dplyr)

# Assuming your dataset is named 'data' and contains columns: location, age, cause, val

# Step 1: Filter data for a specific mental disorder (e.g., Anxiety Disorders)
filtered_data <- gbd_mentalhealth %>%
  filter(cause == "Anxiety disorders")  # Change "Anxiety Disorders" to the desired disorder

# Step 2: Calculate the mean of 'val' (Disability-Adjusted Life Years) across locations and ages
mean_DALYs <- mean(filtered_data$val)

# Optionally, if you want to calculate mean by country:
mean_by_country <- filtered_data %>%
  group_by(country) %>%
  summarise(mean_Anxiety_disorders = mean(val))

# View the mean DALYs for the filtered disorder
print(mean_DALYs)

# View the mean DALYs by country for the filtered disorder
print(mean_by_country)


```







```



